About a VMAF metric

Starting with the version 4.1 of Zond 265, we added the Video Multimethod Assessment Fusion (VMAF) quality metric support. The VMAF metric, developed by Netflix, now appears to have displaced older methods like Peak Signal-to-Noise ratio (PSNR) in Netflix workflows.

The existing metrics, such as PSNR and SSIM do not reflect human perception and Netflix found that "these metrics fail to provide scores that consistently predict the DMOS ratings from observers". DMOS is Differential Mean Opinion Score, the human score achieved in subjective testing.

The new metric works by combining multiple elementary quality metrics and fusing them together with a machine-learning algorithm, specifically a Support Vector Machine (SVM) regressor. The three elementary metrics are:

Visual Information Fidelity (VIF)

Detail Loss Metric (DLM)

Motion

How to measure video quality with VMAF in Zond 265

Launch Zond 265 and open the file

Go to the "Quality" tab

Push "Open reference file" and select file with YUV data

Figure 1. Open reference file button in the "Quality" tab.

To calculate VMAF value for current frame press "Calculate" button. VMAF metric will be calculated for all frames in current GOP and displayed with the green solid line on timeline, as shown on the Figure 2. VMAF value displays in Quality tab.

Figure 2. Calculating VMAF value for current frame.

Clicking the "Press to parse all frames" button starts the metrics' calculation for the entire file. VMAF will be calculated for all frames, as it shown on the Figure 4. Also you can obtain these values selecting the "Quality" from the Bars chart pop-up menu and specifying the path to the reference file.

Figure 3. Bars chart menu.

Figure 4. Calculating VMAF value for all frames.

In order to generate the text report containing VMAF values, you need to select "VMAF" checkbox under the "Quality" section (Figure 5).

Figure 5. VMAF Quality option.

As a result, you'll get the report in either CSV or JSON format. See below the examples of output reports.